By Mutian Xu*, Pei Chen*, Haolin Liu, and Xiaoguang Han
This repository is built for:
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes (ECCV2022 Oral) [arXiv]
If you find our work useful in your research, please consider citing:
@inproceedings{xu2022toscene,
title={TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes},
author={Xu, Mutian and Chen, Pei and Liu, Haolin and Han, Xiaoguang},
booktitle={ECCV},
year={2022}
}
TO-Scene contains 20,740 scenes with three different variants which are TO_Vanilla, TO_Crowd, TO_ScanNet.
- You can download our dataset with the corresponding variants from Google Drive:
Format | TO_Vanilla | TO_Crowd | TO_ScanNet |
---|---|---|---|
ply (point cloud) | Download (4.3GB) | Download (2.1GB) | Download (4.3GB) |
npz (xyz, color, semantic_label, instance_label, bbox) | Download (6.2GB) | Download (2.8GB) | Download (6.5GB) |
- Alternatively, for mainland China users, we also provide Baiduyun link:
Format | TO_Vanilla | TO_Crowd | TO_ScanNet |
---|---|---|---|
ply (point cloud) | Download (4.3GB) | Download (2.1GB) | Download (4.3GB) |
npz (xyz, color, semantic_label, instance_label, bbox) | Download (6.2GB) | Download (2.8GB) | Download (6.5GB) |
You may also need to download meta_data at Google Drive or Baiduyun, including train/val/test split in format of .txt and pre-calculated mean_size_arr (mean size of objects of different classes).
We have released the original CAD placement file (json file, describing the position, scale, orientation of the small objects). You can run your own rendering and reconstruction based on this original annotation file, with parameters (e.g. point density) in your control.
We have provided the code implementations for running 3D semantic segmentation and 3D object detection on our dataset, with the corresponding instructions.
Note that TO-Scene dataset contains 60,174 tabletop object instances from 52 common classes. For reference, we show the classes of these small tabltop objects below, which can be downloaded here as well.
Big furniture
Class | Semantic | Class | Semantic | Class | Semantic |
---|---|---|---|---|---|
3 | cabinet | 9 | window | 24 | refrigerator |
4 | bed | 10 | bookshelf | 28 | showercurtain |
5 | chair | 11 | picture | 33 | toilet |
6 | sofa | 12 | counter | 34 | sink |
7 | table | 14 | desk | 36 | bathtub |
8 | door | 16 | curtain | 39 | garbagebin |
Small tabletop objects
Class | Semantic | Class | Semantic | Class | Semantic | Class | Semantic |
---|---|---|---|---|---|---|---|
41 | bag | 54 | laptop | 67 | chessboard | 80 | mirror |
42 | bottle | 55 | microphone | 68 | coffee_machine | 81 | notebook |
43 | bowl | 56 | microwave | 69 | comb | 82 | pencil |
44 | camera | 57 | mug | 70 | cutting_board | 83 | plant |
45 | can | 58 | printer | 71 | dishes | 84 | plate |
46 | cap | 59 | remote_control | 72 | doll | 85 | radio |
47 | clock | 60 | phone | 73 | eraser | 86 | ruler |
48 | keyboard | 61 | alarm | 74 | eye_glasses | 87 | saucepan |
49 | display | 62 | book | 75 | file_box | 88 | spoon |
50 | earphone | 63 | cake | 76 | fork | 89 | tea_pot |
51 | jar | 64 | calculator | 77 | fruit | 90 | toaster |
52 | knife | 65 | candle | 78 | globe | 92 | vase |
53 | lamp | 66 | charger | 79 | hat | 93 | vegetables |
If you have any questions, please contact Mutian Xu ([email protected]) or Pei Chen ([email protected]).
The dataset challenge will be released soon, via a complete website!